Constrained Optimization Guided Approach for Multispectral and Panchromatic Image Fusion

The remote sensing applications demand the imaging products composed of rich spatial details and high spectral resolution, namely high resolution multispectral (HRMS) images which can not be generated by current generation satellite sensors due to technological and physical constraints. Pansharpening is an image fusion approach that combines the rich geometric details of the panchromatic image and the color information from the multispectral image into a single product. Inspite of the availability of numerous pansharpening techniques, there is a necessity to develop an effective mechanism for the balanced enhancement of spatial and spectral quality in the fused HRMS image. In this paper, a constrained optimization approach for multispectral and PAN image fusion (COAMP) is proposed. The total variation is an efficient operator to extract the geometric features of an image. The spatial differences are exported into the HRMS image with the aid of TV based gradient operator. The spectral information is transferred to the fused image using spectral vectors obtained by taking gradients in the third dimension along the bands of the multispectral image. The optimization problem is formulated and minimized using an efficient solver to get the HRMS image. The comprehensive experiments performed on three different datasets at both reduced scale and full scale resolutions. Additionally, quantitative assessment is also performed using six well-known metrics. The experiments approves the efficacy of the proposed COAMP model.